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All Journal Agrointek Dinamika Kerajinan dan Batik: Majalah Ilmiah
Jatmiko, Tri Hadi
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Agriculture, Biological Sciences & Forestry Arts Biochemistry, Genetics & Molecular Biology Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences

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Optimalisasi kondisi aktivasi arang bonggol jagung sebagai desikan menggunakan metode Taguchi Jatmiko, Tri Hadi; Suherman, Donovan Rendi; Rachman, Rinaldi Medali; Praharasti, Anggita Sari; Prasetyo, Dwi Joko
AGROINTEK Vol 19, No 3 (2025)
Publisher : Agroindustrial Technology, University of Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/agrointek.v19i3.22878

Abstract

Preserving food can be achieved through several methods, one of which is drying. Dried foods are easier to store and distribute. Environmental factors such as temperature and humidity play a crucial role in food preservation and must be controlled to increase its shelf life. A desiccant can be used to regulate humidity levels. Activated carbon is a material that can be used as a desiccant, which can be produced from biomass such as corn cobs. This study focuses on optimizing the conditions for using corncob charcoal as a desiccant by using a Taguchi orthogonal array. The optimization process involves determining the optimal KOH concentration, immersion time, and the ratio of charcoal to KOH volume. Results showed that KOH concentration was the most significant factor in activating carbon from corn cobs, followed by the ratio and immersion time. The optimal conditions for activating carbon from corn cobs as a desiccant were found to be a KOH concentration of 2%, a ratio of 10 g charcoal to 150 mL KOH, and an immersion time of 2 hours, resulting in a water vapor adsorption capacity of 0.3949 g/g
Prediksi Adsorpsi Zat Warna Metilene Biru Pada Karbon Aktif Menggunakan Machine Learning Jatmiko, Tri Hadi
Dinamika Kerajinan dan Batik: Majalah Ilmiah Vol. 39 No. 1 (2022): DINAMIKA KERAJINAN DAN BATIK : MAJALAH ILMIAH
Publisher : Balai Besar Standardisasi dan Pelayanan Jasa Industri Kerajinan dan Batik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22322/dkb.v39i1.6936

Abstract

Pemanfaatan teknologi informasi dalam membantu mengurangi kegiatan eksperimen di laboratorium semakin meningkat. Pemanfaatan teknologi informasi akan menghemat bahan, waktu dan biaya, serta tentunya akan mengurangi resiko bahaya yang kemungkinan timbul jika dilakukan percobaan secara langsung di laboratorium. Salah satu teknologi informasi yang sedang berkembang dan menarik untuk digunakan adalah machine learning. Penelitian ini bertujuan untuk mengetahui unjuk kerja model machine learning dalam memprediksi adsorpsi metilene biru pada karbon aktif. Dataset adsorpsi metilene biru menggunakan karbon aktif biji alpukat dari penelitian terdahulu yang dilakukan oleh Regti dkk (2017) dibagi menjadi data latihan dan data tes. Dengan data tersebut kemudian digunakan untuk menguji performa model machine learning (neural networks, support vector machines, random forest, dan linear regression) menggunakan metode Stratified 10-fold Cross validation. Model neural network merupakan model machine learning terbaik untuk memprediksi proses adsorpsi metilene biru pada karbon aktif dibandingkan dengan model SVM, Random Forest maupun regresi linier dengan performa prediksi terbaik pada data latihan maupun data uji. Nilai MSE 14.640, RMSE 3.826, MAE 3.178 dan R2 0.975 pada data latihan dan pada data uji nilai MSE 3.51e-06, RMSE 0.00187, MAE 0.00135 dan R2 0.999.
Co-Authors Praharasti, Anggita Sari Prasetyo, Dwi Joko Rachman, Rinaldi Medali Suherman, Donovan Rendi